Systematic Sampling: Definition, Examples, and Types

Explore how systematic sampling helps researchers by providing a structured method to select representative samples from larger populations, ensuring efficiency and reducing bias.

Author

Soham Saha

Date

June 2, 2024

Systematic sampling implies a statistical method of obtaining a sample from a larger population. In most cases, the sample is taken systematically and randomly. The method is widely used by researchers and analysts who aim to implement data collection from some broader population without contacting every individual. Also, the method is advantageous when the population is extensive, diverse, or inaccessible. 

The blog will broadly describe the systematic sampling method, including its definition and advantages. It will also contain examples of its employment in various research and data analysis studies and guidelines on applying the method effectively.

What is Systematic Sampling?

Systematic sampling is a statistical sampling method in which researchers select every nth member from a population list after randomly choosing a starting point. This ensures that each member of the population has an equal chance of being selected, making it a systematic and structured approach to creating a representative sample.

Example of systematic sampling

Imagine a situation where a researcher needs to survey customers in a shopping mall regarding their shopping experience. They decided to use systematic sampling rather than surveying every customer, which may be infeasible. 

To employ this method, the researcher must start by selecting a random point—for example, an entry point into the building. Then, they will survey every nth customer who enters, with n being the sampling interval. In this way, every customer will have an equal chance of being selected, creating a sample representative of the mall’s entire customer population.

How Does Systematic Sampling Work?

Systematic sampling randomly selects every nth item from the population list after determining a starting point. 

For example, if a researcher wants to survey a population of 1000 people and prefers a sample size of 100 with a sampling interval of 10, they would choose a starting point between the first and tenth person. After that, they would take every tenth person until they reached the sample size. Systematic sampling guarantees that each item on the list of a population is selected and has an equal chance of being sampled in the survey.

Types of Systematic Sampling

Systematic sampling typically involves selecting every nth element from a population list after randomly determining a starting point. However, variations can occur based on how the starting point is determined and the specific selection method.

Here are some of the different types of systematic sampling:

Simple Systematic Sampling

This is the simplest and easiest form of systematic sampling. Here, the researchers choose a random starting point, and then all elements follow every nth component of the population list until the desired sample size is reached. This will convert the nth element to every first element of the list. For example, to choose every 10th item from a population list of 1000, the researcher has to choose a random starting point between 1 to 10th, and every 10th item after that will be selected.

Randomized Systematic Sampling

Randomized systematic sampling is similar to simple systematic methods, but the starting point is selected randomly and not predetermined, ensuring that the selection is free of bias. Researchers can employ various techniques to select a random starting point, from random number generators to other creative means. In this case, each population element has an equal chance of being in the sample.

Interval Systematic Sampling

Unlike the previous methods, interval systematic sampling presupposes choosing items at fixed intervals without the requirement to start from a random point. For example, if the researcher wants to sample every fifth item from 1000, the first item is sampled first, so the fixed interval is observed afterward. Choosing a specific interval for the selection, this method lacks randomization at the starting point, which shows that the method is systematic and relies on regular intervals between selection points.

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Advantages of Systematic Sampling

Systematic sampling offers several advantages, some of which are mentioned below:

Efficiency

One of the major advantages of systematic sampling is that it is efficient. When calculated, the sampling interval can be used by the researchers to identify their sample units easily and quickly without any complicated randomization steps. It is especially beneficial when the population studied is large since choosing a sample of this size without a systematic approach can take much time.

Ease of Implementation

Compared to other sampling methods, such as stratified or cluster sampling, systematic sampling is relatively straightforward. Researchers only need to determine the sampling interval and select a random starting point if randomization is desired. This simplicity makes systematic sampling accessible to researchers with varying statistical expertise and resources.

Reduced Sampling Bias

Systematic sampling can reduce certain types of bias that may arise in other sampling methods, such as convenience sampling. By systematically selecting sample elements from the population, researchers minimize the risk of inadvertently favoring certain characteristics or groups within the population. However, it's essential to ensure that the systematic sampling process does not introduce its own bias, such as patterns in the population list that could affect the sample's representativeness.

Representativeness

Systematic sampling can yield a sample that accurately represents the larger population when implemented correctly. This is because each element in the population has an equal chance of being included in the sample, provided that the sampling interval is appropriately chosen and the initial starting point is randomly determined or adequately distributed. As a result, systematic sampling can produce reliable estimates and insights about the population characteristics.

Uniform Coverage

Systematic sampling ensures uniform population coverage by regularly selecting sample elements. Unlike other sampling methods, which may result in uneven coverage or overrepresentation of certain population segments, systematic sampling strives to sample the entire population evenly. This helps ensure that all relevant subgroups or characteristics are adequately represented in the sample, contributing to the sample's overall representativeness and reliability.

When To Use Systematic Sampling?

Systematic sampling is particularly useful in the following scenarios:

Large Populations

When dealing with large populations where it's impractical to survey or study every individual, systematic sampling offers an efficient way to select a representative sample.

Resource Constraints

When resources such as time, money, or personnel are limited, systematic sampling can balance efficiency and accuracy, requiring fewer resources than other sampling methods.

Ordered Populations

Systematic sampling can be especially effective when the population is naturally ordered or arranged in a sequence, such as a list of students in a school or customers in a database. It ensures that each element in the population has an equal chance of being selected while maintaining a systematic approach.

Desired Balance of Efficiency and Representativeness

Systematic sampling balances efficiency and representativeness, making it suitable for research projects where both factors are important. It's often chosen when researchers seek a reasonably accurate population representation while minimizing sampling effort.

Routine Monitoring or Quality Control

When regular monitoring or quality control measures are required, systematic sampling can be applied to ensure consistent and systematic data collection over time. This is commonly seen in manufacturing processes, customer satisfaction surveys, or environmental monitoring efforts.

Final Words

Systematic sampling is a practical and efficient method for selecting representative samples from large populations. Its structured approach and ability to balance efficiency and representativeness make it a valuable tool for researchers across diverse fields. 

By selecting sample elements regularly, systematic sampling ensures uniform population coverage while reducing bias and resource expenditure. Whether applied in market research, quality control, or social sciences, systematic sampling provides researchers with a reliable means of obtaining valuable insights while optimizing sampling efforts.

Sampling comes with its challenges and limitations, including bias and error. However, Decode collaborates with panel providers like Lucid, Cint, and Dynata. We have overcome these hurdles and created one of the largest Insights AI communities. With access to over 80 million respondents across 120 countries, researchers can easily find the right audience for their studies, segmented by demographics, geography, or psychographics, ensuring targeted and insightful research outcomes.

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Frequently Asked Questions

What is the systematic sampling method?

The systematic sampling method involves selecting every nth member from a population list after a random starting point to create a representative sample.

What are systematic and stratified sampling?

Systematic sampling involves selecting every nth member from a population list after a random starting point. In contrast, stratified sampling divides the population into distinct subgroups and systematically selects samples from each subgroup.

What is the difference between systematic sampling and random sampling?

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Systematic sampling selects samples from a population list at regular intervals after a random starting point, ensuring every member has an equal chance of being selected, whereas random sampling selects members purely by chance.

What is another name for systematic sampling?

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Another name for systematic sampling is interval sampling, which involves selecting samples from a population list at fixed intervals.

What is called stratified sampling?

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Stratified sampling is a method in which the population is divided into distinct subgroups, or strata, and random samples are taken from each subgroup to ensure representation of all segments of the population.

Author Bio

Soham is a true Manchester United fan who finds joy in more than just football. Whether navigating the open road, scoring virtual goals in FIFA, reading novels, or enjoying quality time with friends, Soham embraces a life full of diverse passions.

Soham Saha

Product Marketing Specialist

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